A method to extract and track the position of a guide wire during endovascular interventions under x-ray fluoroscopy is already known from the publication entitled “2-D Guide wire tracking during endovascular interventions” by Shirley A. M. Baert, in “Medical Image Computing and Computer-Assisted Intervention”—MICCAI 2000, S. L. Delp, A. M. DiGioia, B. Jaramaz (eds), vol. 1935 of Lecture Notes in Computer Science, Springer-Verlag, Berlin, 2000, pp. 727-734. This publication describes a method that can be used in low quality fluoroscopic images to estimate the position of the guide wire in world coordinates. A two-step procedure is utilized to track the guide-wire in subsequent frames. In a first step, a rough estimate of the displacements of the guide wire is obtained using a template matching procedure on a spline model. In a second step, the position of the guide-wire is optimized, by fitting the guide-wire to a feature image in which line-like structures are enhanced. In this optimization step, the influence of the scale at which the feature is calculated and the additional value of using directional information is investigated. The method is applied both on the original and subtraction images. Using proper parameter settings, the guide-wire could be successfully tracked based on the original images, in 141 out of 146 frames from 5 image sequences.
A method for extracting stents in medical images is already known from the publication entitled “Deformable Boundary Detection of Stents in Angiographic Images”, by Ioannis Kompatsiaris et alii, in IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 19, No. 6, June 2000, pages 652-662. This document describes an image processing method for deformable boundary detection of medical tools, called stents, in angiographic images. A stent is a surgical stainless steel coil that is placed in the artery in order to improve blood circulation in regions where a stenosis has appeared. Assuming initially a set of three-dimensional (3-D) models of stents and using perspective projection of various deformations of the 3-D model of the stent, a large set of two-dimensional (2-D) images of stents is constructed. These synthetic images are then used as a training set for deriving a multi-variate density estimate based on eigenspace decomposition and formulating a maximum-likelihood estimation framework in order to reach an initial rough estimate for automatic object recognition. Then, the silhouette of the detected stent is refined using a 2-D active contour (snake) algorithm, integrated with an iterative initialization technique, which takes into consideration the geometry of the stent. As disclosed in the cited publication, when a narrowing called stenosis is identified in a coronary artery of a patient, a procedure called angioplasty may be prescribed to improve blood flow to the heart muscle by opening the blockage. In recent years, angioplasty increasingly employs a stent implantation technique. This stent implantation technique includes an operation of stent placement at the location of the detected stenosis in order to efficiently hold open the diseased vessel, as illustrated by FIG. 2 of the cited publication. Stent placement helps many patients to avoid emergency heart bypass surgery and/or heart attack (myocardial infraction). The stent, as illustrated by FIG. 1 of the cited publication, is a small, slotted, stainless steel tube cut by a precision laser for forming a coil. It is wrapped tightly around a balloon attached to a monorail introduced by way of a catheter and a guide-wire forming a device called balloon-tipped catheter. This balloon-tipped catheter is introduced into the artery through a small incision. Once in place, the balloon is inflated in order to expand the coil. Once expanded, the stent, which can be considered as a permanent implant, acts like a scaffold keeping the artery wall open. This allows more blood flow to the heart muscle.